Core Usage

🏗 Defining ExoModels

Inherit from ExoModel to give your data structures "AI powers." Use Pydantic's Field to provide descriptions that the LLM will use to understand the context of each field.

from exomodel import ExoModel
from pydantic import Field

class Project(ExoModel):
    title: str = Field(description="The name of the project")
    budget: float = Field(description="Estimated cost in USD")

🧠 AI Interactions

Initialization by Prompt

You can instantiate a model directly from a natural language description.

project = Project(prompt="A web application for a futuristic pet store using React and Python.")

Self-Analysis with RAG

ExoModels can analyze themselves against specific business rules provided via RAG.

class BusinessDocument(ExoModel):
    content: str

    @classmethod
    def get_rag_sources(cls):
        return ["docs/compliance_rules.pdf"]

doc = BusinessDocument(content="Draft content...")
analysis = doc.run_analysis()

Field Updates

Update specific fields using natural language.

project.update_field("budget", "Increase the budget by 20% due to scope expansion.")

📋 Managing Collections

Use ExoModelList to handle multiple entities, enabling bulk generation and UI-ready exports.

from exomodel_list import ExoModelList

projects = ExoModelList(item_class=Project)
projects.create_list("Generate 5 innovative AI startup ideas.")

# Export to CSV
projects.to_csv("startups.csv")

# UI Representation
print(projects.to_ui())